“MLOps Roadmap | How to learn MLOps” by Felipe introduces viewers to a structured roadmap designed to guide aspiring MLOps engineers through the skills and knowledge needed to excel in the field. Felipe, a computer vision engineer, explains that although he’s not an MLOps expert, he created this roadmap to become more acquainted with MLOps, a field that combines machine learning and DevOps practices.
He details his collaboration with ChatGPT to design a roadmap that is simple, focused, and practical, consisting of three modules with four skills each. These modules are:
- Machine Learning Fundamentals: Covering Python basics, foundations of machine learning, data processing, feature engineering, model training, and evaluation.
- Intermediate Machine Learning and MLOps Foundations: Diving deeper into machine learning with advanced skills, including deep learning technologies, model optimization, Git fundamentals, Docker usage, and the basics of continuous integration and deployment (CI/CD).
- Advanced MLOps: Focusing on high-level MLOps skills like Infrastructure as Code (using Terraform or AWS CloudFormation), Kubernetes, Prometheus, Grafana, and securing pipelines.
Felipe emphasizes the importance of both machine learning proficiency and DevOps-related skills for MLOps engineers. He provides publicly available resources for each skill, making the roadmap accessible to everyone interested in MLOps for free. Felipe’s narrative underscores the growing popularity of MLOps and his personal journey to demystify it, making this roadmap a valuable resource for learners at different stages of their machine learning and DevOps education.
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